The following maps all use data from the 2020 CalEnviroScreen survey. For this assignment, we focus on asthma and PM2.5 concentration. The maps below show PM2.5 concentration and asthma rates throughout the desired region (SF bay area).

Below: scatter plot comparing asthma vs. PM2.5 concentration. I think it makes sense that the line of best fit is positively correlated between the two, I think that this is a rather loose correlation since the data is rather varied - this is likely due to the fact that asthma is not created by PM2.5 concentration or lack thereof alone, and is also influenced by genetic factors and other environmental influences.

An increase of prevelance in PM2.5 is associated with an increase of prevelence in asthma”; “20% of the variation in asthma is explained by the variation in PM2.5. 

## 
## Call:
## lm(formula = log(Asthma) ~ PM2.5, data = bay_asthma_pm_tract)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.00402 -0.46479  0.03313  0.42298  1.75525 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.69234    0.22840   3.031  0.00248 ** 
## PM2.5        0.35633    0.02686  13.264  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6566 on 1578 degrees of freedom
## Multiple R-squared:  0.1003, Adjusted R-squared:  0.09974 
## F-statistic: 175.9 on 1 and 1578 DF,  p-value: < 2.2e-16